AI- DRIVEN WORKPLACE HAZARD DETECTION

The rapid advancement of artificial intelligence (AI) has introduced trans-formative capabilities across various industries, particularly in enhancing occupational safety through intelligent hazard detection. This research project explores the development and assessment of AI-driven workplace hazard detection systems, which utilize technologies such as computer vision, machine learning algorithms, and IoT-enabled sensors to identify, analyze, and mitigate risks in real-time. The study specifically investigates the effectiveness of such systems in improving workplace safety, reducing human error, and fostering proactive risk management practices. The proposed system is conceptualized to automatically detect workplace hazards, such as equipment malfunctions and unsafe human behaviors, using smart sensors and AI models trained to recognize patterns associated with danger. Grounded in Machine Learning and Information Retrieval theories, the study evaluates how these systems can enhance traditional safety protocols through real-time alerts, predictive analytics, and automated decision support. The research provides actionable recommendations for implementing AI-driven safety technologies while emphasizing the need for human oversight, proper employee training, and ethical governance. This study contributes to the growing body of knowledge on AI applications in safety-critical environments and underscores the potential of AI to revolutionize workplace safety management.